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eduardzamfir committed May 24, 2024
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version.py

# ignored files with suffix
*.html
*.png
*.jpeg
*.jpg
*.gif
*.pth
*.zip

# template

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pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
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219 changes: 219 additions & 0 deletions index.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="See More Details: Efficient Image Super-Resolution by Experts Mining">
<meta name="keywords" content="Mixture-of-Experts, Super-Resolution, Efficiency">
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<title>Efficient Image Super-Resolution by Experts Mining</title>

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<section class="hero">
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<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-2 publication-title">See More Details: Efficient Image Super-Resolution by Experts Mining</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://eduardzamfir.github.io">Eduard Zamfir<sup>1</sup></a>
</span>
<span class="author-block">
<a href="https://sites.google.com/view/zwwu/accueil">Zongwei Wu<sup>1*</sup></a>
</span>
<span class="author-block">
<a href="https://scholar.google.co.in/citations?user=WwdYdlUAAAAJ&hl=en">Nancy Mehta<sup>1</sup></a>
</span>
<span class="author-block">
<a href="https://yulunzhang.com">Yulun Zhang<sup>2,3*</sup></a>
</span>
<span class="author-block">
<a href="https://www.informatik.uni-wuerzburg.de/computervision/home/"><sup>1</sup>Radu Timofte</a>
</span>
</div>

<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of Würzburg, Germany</span>
<span class="author-block"><sup>2</sup>ETH Zürich, Switzerland</span>
<span class="author-block"><sup>3</sup>Shanghai Jiao Tong University, China</span>
<br>
<small>ICML 2024, Vienna </small>
<br>
<small><sup>*</sup>Corresponding authors</small>
</div>

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<span>arXiv</span>
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</section>

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<style>
.before,
.after { margin: 0; }

.before figcaption,
.after figcaption {
background: #fff;
border: 1px solid #c0c0c0;
border-radius: 12px;
color: #2e3452;
opacity: 0.8;
padding: 12px;
position: absolute;
top: 50%;
transform: translateY(-50%);
line-height: 100%;
}

.before figcaption {left: 12px;}
.after figcaption { right: 12px;}
</style>

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<img-comparison-slider tabindex="0" class="slider-example-split-line rendered" hover="hover" >
<figure slot="first" class="before">
<img src="figures/assets/img004_x4.png" width=100%>
<figcaption><strong>Before</strong></figcaption>
</figure>
<figure slot="second" class="after">
<img src="figures/assets/img004_x4_SeemoRe.png" width=100%>
<figcaption><strong>Ours</strong></figcaption>
</figure>
</img-comparison-slider>
</div>

<div class="columns is-centered">

</div>

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<!-- Abstract. -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a significant challenge in image super-resolution (SR). While recent approaches have demonstrated the efficacy of intricate operations customized for various objectives, the straightforward stacking of these disparate operations can result in a substantial computational burden, hampering their practical utility. In response, we introduce **S**eemo**R**e, an efficient SR model employing expert mining. Our approach strategically incorporates experts at different levels, adopting a collaborative methodology. At the macro scale, our experts address rank-wise and spatial-wise informative features, providing a holistic understanding. Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts. By tapping into experts specialized in distinct key factors crucial for accurate SR, our model excels in uncovering intricate intra-feature details. This collaborative approach is reminiscent of the concept of **see more**, allowing our model to achieve an optimal performance with minimal computational costs in efficient settings
</p>
</div>
</div>
</div>
<!--/ Abstract. -->

<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{zamfir2024details,
title={See More Details: Efficient Image Super-Resolution by Experts Mining},
author={Eduard Zamfir and Zongwei Wu and Nancy Mehta and Yulun Zhang and Radu Timofte},
journal={ICML},
year={2024},
}
}</code></pre>
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</section>


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